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Found 6 papers, 1 papers with code
PaRaDe: Passage Ranking using Demonstrations with Large Language Models
Recent studies show that large language models (LLMs) can be instructed to effectively perform zero-shot passage re-ranking, in which the results of a first stage retrieval method, such as BM25, are rated and reordered to improve relevance.
Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank
In this work, we extend LIME to propose Rank-LIME, a model-agnostic, local, post-hoc linear feature attribution method for the task of learning to rank that generates explanations for ranked lists.
You can't pick your neighbors, or can you? When and how to rely on retrieval in the $k$NN-LM
Retrieval-enhanced language models (LMs), which condition their predictions on text retrieved from large external datastores, have recently shown significant perplexity improvements compared to standard LMs.
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Language Modelling
on WikiText-103
Explaining Documents' Relevance to Search Queries
GenEx explains a search result by providing a terse description for the query aspect covered by that result.
Query-driven Segment Selection for Ranking Long Documents
Transformer-based rankers have shown state-of-the-art performance.
Mixed Attention Transformer for Leveraging Word-Level Knowledge to Neural Cross-Lingual Information Retrieval
In the monolingual retrieval task, because of the same lexical inputs, it is easier for model to identify the query terms that occurred in documents.
